Urban network-wide traffic volume estimation under sparse deployment of detectors
Sensing network-wide traffic information is fundamental for the sustainable development of
urban planning and traffic management. However, owing to the limited budgets or device …
urban planning and traffic management. However, owing to the limited budgets or device …
A customized data fusion tensor approach for interval-wise missing network volume imputation
Traffic missing data imputation is a fundamental demand and crucial application for real-
world intelligent transportation systems. The wide imputation methods in different missing …
world intelligent transportation systems. The wide imputation methods in different missing …
Proposal of a machine learning approach for traffic flow prediction
M Berlotti, S Di Grande, S Cavalieri - Sensors, 2024 - mdpi.com
Rapid global urbanization has led to a growing urban population, posing challenges in
transportation management. Persistent issues such as traffic congestion, environmental …
transportation management. Persistent issues such as traffic congestion, environmental …
Towards better traffic volume estimation: Jointly addressing the underdetermination and nonequilibrium problems with correlation-adaptive GNNs
Traffic volume is an indispensable ingredient to provide fine-grained information for traffic
management and control. However, due to the limited deployment of traffic sensors …
management and control. However, due to the limited deployment of traffic sensors …
Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner
Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the
multiscale transportation system. Existing methods aim to reconstruct STTD using low …
multiscale transportation system. Existing methods aim to reconstruct STTD using low …
Pmgcn: Progressive multi-graph convolutional network for traffic forecasting
Z Li, Y Han, Z Xu, Z Zhang, Z Sun, G Chen - ISPRS International Journal …, 2023 - mdpi.com
Traffic forecasting has always been an important part of intelligent transportation systems. At
present, spatiotemporal graph neural networks are widely used to capture spatiotemporal …
present, spatiotemporal graph neural networks are widely used to capture spatiotemporal …
DeepAD: An integrated decision-making framework for intelligent autonomous driving
Autonomous vehicles have the potential to revolutionize intelligent transportation by
improving traffic safety, increasing energy efficiency, and reducing congestion. In this study …
improving traffic safety, increasing energy efficiency, and reducing congestion. In this study …
[HTML][HTML] 图神经网络在交通预测中的应用综述
户佐安， 邓锦程， 韩金丽， 袁凯 - 交通运输工程学报, 2023 - transport.chd.edu.cn
为寻求提升交通预测时空计算任务性能的有效途径, 探索图神经网络技术在交通预测中的应用
前景和挑战, 回顾了交通预测方法的发展, 总结了模型驱动方法, 统计模型, 传统机器学习方法和 …
前景和挑战, 回顾了交通预测方法的发展, 总结了模型驱动方法, 统计模型, 传统机器学习方法和 …
Dynamic Origin-Destination Flow Imputation Using Feature-Based Transfer Learning
Real-time and full-sample vehicle origin-destination (OD) information is essential for traffic
management and control in urban road network. However, the low coverage of automatic …
management and control in urban road network. However, the low coverage of automatic …
A Data Fusion CANDECOMP-PARAFAC Method for Interval-wise missing network volume imputation
Traffic missing data imputation is a fundamental demand and crucial application for real-
world intelligent transportation systems. The wide imputation methods in different missing …
world intelligent transportation systems. The wide imputation methods in different missing …